System, Method, and Computer Program Product for Generating Real-Time Predictions for Vehicle Transportation Requests Using Machine Learning

ABSTRACT

Described are a system, method, and computer program product for generating real-time predictions for vehicle transportation requests using machine learning. The method includes generating, with a processor and a machine-learning classification model, a transportation categorization for each consumer of a plurality of consumers based on historic transaction data; processing a plurality of new transactions by each consumer, each new transaction associated with a geographic node of activity; in response to processing the new transactions, generating a plurality of vehicle transportation predictions for the consumers based on the transportation categorization for each consumer and a geographic node of activity associated with a new transaction, each vehicle transportation prediction representing a likelihood that the consumer will request vehicle transportation subsequent to conducting the new transaction; and generating a supply map interface comprising a visual identification of a location in which a number of requests for vehicle transportation is predicted to increase based on the vehicle transportation predictions.

BACKGROUND Field

Disclosed embodiments relate generally to computer-implementedpredictions for vehicle transportation requests, and in somenon-limiting embodiments or aspects, to a system, method, and computerprogram product for generating real-time predictions for vehicletransportation requests using machine learning.

Technical Considerations

Traffic congestion is a common problem with serious economic andenvironmental implications. Every year, travelers around the worldcumulatively spend billions of hours stuck in traffic—time that couldotherwise be used productively. Moreover, as vehicles spend more time onthe road due to traffic, more energy is consumed to power thosevehicles, and more vehicle emissions are produced. Such traffic problemsare not limited to roadway drivers, either, and the traffic problemsaffect several networks, including transport by train, bus, ferry, taxi,and other modes of transportation. Existing traffic-avoidance solutionsare usually focused on identifying traffic already in progress andoptimizing a route based on known traffic conditions. For example,traffic may be self-reported, by other travelers in a network, ordeduced based on geo-tracking travelers in real-time. Such solutions,however, are generally reactionary (not predictive), and the solutionsare largely unable to re-route the first to arrive at a traffic event.More predictive traffic-avoidance systems may take into account theregular ebbs and flows of traffic that occur at certain times of day ordays of the week, but those systems may not predict irregular,unscheduled, and/or non-seasonal changes in traffic.

There is a need in the art for predictive traffic avoidance systems thatcan anticipate changes in traffic based on additional factors beyondcurrent or historical traffic conditions. There is a need in the art forsuch systems to predict traffic changes substantially in real-time andto communicate route changes to navigational systems in the region inadvance of predicted traffic events.

In addition, in recent years, consumers have become increasingly relianton vehicles-for-hire as opposed to personal vehicles for a number ofreasons, including improvements in ride-hailing technology and increasedconcentrations of populations in urban centers. Expanded utilization ofvehicles-for-hire produces a number of societal benefits, such asreducing the total number of vehicles on increasingly crowded streetsand allowing space that otherwise would be occupied by parked vehiclesto be used for other purposes. However, reliance on vehicles-for-hirecan often cost consumers valuable time due to the intrinsic delays thatare encountered when attempting to locate and engage a vehicle-for-hirein person or due to the time that is required for a vehicle-for-hirethat has been requested electronically (e.g., via a telephone call orthrough a mobile application) to be dispatched and sent to the correctlocation to pick up the consumer. Further, for operators ofvehicles-for-hire, valuable time often may be wasted when attempting tolocate potential customers in person or when the vehicle is travelingfrom a dispatch location to the location of a customer who has requestedtransportation electronically.

Existing systems that attempt to address these issues generally rely onboth the potential customer and vehicle operator using a dedicatedcomputer program, such as a mobile application, and a vehicle operatorgenerally determines the locations of potential customers based on datareceived from devices associated with the potential customers (e.g., GPSdata and/or user-input location data). However, such systems haveseveral technical limitations. For example, such systems generallycannot determine the locations of potential customers who are notactively using a dedicated computer program, and are accordingly of nouse for engaging customers in-person. Further, such systems arereactive, rather than predictive, such that a location of a potentialcustomer is not determined until the potential customer is alreadyseeking a ride. Accordingly, surges in demand at certain locations(e.g., due to an event that the vehicle-for-hire operator may not beaware of) are not accounted for until many customers have alreadytransmitted ride requests, and this can lead to increased wait time forpotential customers and wasted time and/or lost business opportunitiesfor operators of vehicles-for-hire.

There is a need in the art for predictive systems for allowing anoperator of a vehicle-for-hire to identify optimal locations forengaging new customers that can anticipate changes in demand based onadditional factors beyond current or historical conditions. There is aneed in the art for such systems to predict changes in demand forvehicles-for-hire substantially in real-time and to communicate routechanges to navigational systems associated with such vehicles in theregion in advance of predicted instances of increased demand.

SUMMARY

Accordingly, and generally, provided is an improved system,computer-implemented method, and computer program product formachine-learning-based traffic prediction. Preferably, provided is asystem, computer-implemented method, and computer program product forreceiving historic transaction data including a plurality oftransactions and generating, using a machine-learning classificationmodel, a transportation categorization for at least one consumer.Preferably, provided is a system, computer-implemented method, andcomputer program product for receiving at least one message associatedwith at least one transaction, identifying at least one geographic nodeof activity in the region, and generating an estimate of trafficintensity for the at least one geographic node of activity. Preferably,provided is a system, computer-implemented method, and computer programproduct for comparing the estimate of traffic intensity to a thresholdof traffic intensity and, in response to determining that the estimateof traffic intensity satisfies the threshold: generating a communicationconfigured to cause at least one navigation device to modify anavigation route; and communicating the communication to the at leastone navigation device.

Additionally, and generally, provided is an improved system,computer-implemented method, and computer program product for generatingreal-time predictions for vehicle transportation requests using machinelearning. Preferably, provided is a system, computer-implemented method,and computer program product for generating, with at least one processorand a machine-learning classification model, a transportationcategorization for each consumer of a plurality of consumers based atleast partially on historic transaction data including a plurality oftransactions by each of the plurality of consumers, each plurality oftransactions for each consumer including a subset of transactions forvehicle transportation. Preferably, provided is a system,computer-implemented method, and computer program product forprocessing, with at least one processor, a plurality of new transactionsby each consumer of the plurality of consumers, each new transaction ofthe plurality of new transactions associated with at least onegeographic node of activity. Preferably, provided is a system,computer-implemented method, and computer program product forgenerating, with at least one processor and the machine-learningclassification model, a plurality of vehicle transportation predictionsfor the plurality of consumers, each vehicle transportation predictionbased on the transportation categorization for each consumer and ageographic node of activity associated with a new transaction, eachvehicle transportation prediction representing a likelihood that theconsumer will request vehicle transportation subsequent to conductingthe new transaction. Preferably, provided is a system,computer-implemented method, and computer program product forgenerating, with at least one processor, a supply map interfaceincluding at least one visual identification of at least one location inwhich a number of requests for vehicle transportation is predicted toincrease based on the plurality of vehicle transportation predictions.

According to one non-limiting embodiment or aspect, provided is acomputer-implemented method for machine-learning-based trafficprediction. The method includes receiving, with at least one processor,historic transaction data including a plurality of transactions by atleast one consumer. The method also includes generating, with at leastone processor and using a machine-learning classification model, basedat least partially on the historic transaction data, a transportationcategorization for the at least one consumer representative of a mode oftransportation to be taken by the at least one consumer when the atleast one consumer travels to complete a transaction at a point-of-saleterminal. The method further includes receiving, with at least oneprocessor, at least one message associated with at least one transactionbetween the at least one consumer and at least one point-of-saleterminal in a region. The method further includes identifying, with atleast one processor, at least one geographic node of activity in theregion including the at least one point-of-sale terminal. The methodfurther includes generating, with at least one processor and based atleast partially on the at least one message and the transportationcategorization of the at least one consumer, an estimate of trafficintensity for the at least one geographic node of activity. The estimateof traffic intensity is representative of a predicted volume of trafficfor a mode of transportation. The method further includes comparing,with at least one processor, the estimate of traffic intensity for theat least one geographic node of activity to a threshold of trafficintensity for the at least one geographic node of activity. The methodfurther includes, in response to determining that the estimate oftraffic intensity for the at least one geographic node of activitysatisfies the threshold: generating, with at least one processor, acommunication to at least one navigation device configured to cause theat least one navigation device to modify a navigation route; andcommunicating, with at least one processor, the communication to the atleast one navigation device.

In further non-limiting embodiments or aspects, the generating of thetransportation categorization may include associating at least one typeof transaction with at least one mode of transportation. The at leastone mode of transportation may include at least transit by motorvehicle, and the at least one type of transaction may include at leastone of: gasoline purchase, toll road payment, vehicle purchase or leasepayment, vehicle repair service, vehicle maintenance service, or anycombination thereof.

In further non-limiting embodiments or aspects, the at least onepoint-of-sale terminal may include a plurality of point-of-saleterminals, the at least one geographic node of activity may include aplurality of geographic nodes of activity, and each geographic node ofactivity of the plurality of geographic nodes of activity may include asubset of point-of-sale terminals of the plurality of point-of-saleterminals. The region may include at least one channel of transit bywhich a traveler is able to travel to a point-of-sale terminal of the atleast one geographic node of activity, and the threshold of trafficintensity may be predetermined and based at least partially on acapacity of traffic of the at least one channel of transit. The at leastone channel of transit may include a plurality of roads, and thecapacity of traffic may be based at least partially on a volume ofvehicular traffic capable of traveling along the plurality of roads.

In further non-limiting embodiments or aspects, the communication to theat least one navigation device may be relayed through at least onelocalized communication device positioned in a subregion of the region.The at least one localized communication device may be programmed and/orconfigured to communicate with navigation devices in the subregion tofacilitate generation of navigation routes for travelers in thesubregion. The at least one localized communication device may include aplurality of localized communication devices forming a subregionalcommunication network. Each localized communication device of theplurality of localized communication devices may be programmed and/orconfigured to communicate with navigation devices and other localizedcommunication devices to facilitate generation of navigation routes fortravelers in the subregion.

In further non-limiting embodiments or aspects, the method may includeassociating, with at least one processor, the at least one geographicnode of activity with a category of traffic intensity. The method mayalso include generating, with at least one processor, display dataconfigured to cause the at least one navigation device to display the atleast one geographic node of activity as at least one point on ageographical map of the region. A visual characteristic of the at leastone point may represent the category of traffic intensity. The at leastone message may include a plurality of transaction authorizationrequests in an electronic payment processing network occurring inreal-time. The at least one navigation device may be associated with atleast one consumer who is traveling through the region via thenavigation route.

According to one non-limiting embodiment or aspect, provided is a systemfor machine-learning-based traffic prediction. The system includes atleast one server computer including at least one processor. The at leastone server computer is programmed and/or configured to receive historictransaction data including a plurality of transactions by at least oneconsumer. The at least one server computer is also programmed and/orconfigured to generate, using a machine-learning classification modeland based at least partially on the historic transaction data, atransportation categorization for the at least one consumerrepresentative of a mode of transportation to be taken by the at leastone consumer when the at least one consumer travels to complete atransaction at a point-of-sale terminal. The at least one servercomputer is further programmed and/or configured to receive at least onemessage associated with at least one transaction between the at leastone consumer and at least one point-of-sale terminal in a region. The atleast one server computer is further programmed and/or configured toidentify at least one geographic node of activity in the regionincluding the at least one point-of-sale terminal. The at least oneserver computer is further programmed and/or configured to generate,based at least partially on the at least one message and thetransportation categorization of the at least one consumer, an estimateof traffic intensity for the at least one geographic node of activity,the estimate of traffic intensity representative of a predicted volumeof traffic for a mode of transportation. The at least one servercomputer is further programmed and/or configured to compare the estimateof traffic intensity for the at least one geographic node of activity toa threshold of traffic intensity for the at least one geographic node ofactivity. The at least one server computer is further programmed and/orconfigured to, in response to determining that the estimate of trafficintensity for the at least one geographic node of activity satisfies thethreshold: generate a communication to at least one navigation deviceconfigured to cause the at least one navigation device to modify anavigation route; and communicate the communication to the at least onenavigation device.

In further non-limiting embodiments or aspects, the generating of thetransportation categorization may include associating at least one typeof transaction with at least one mode of transportation. The at leastone mode of transportation may include at least transit by motor vehicleand the at least one type of transaction may include at least one of:gasoline purchase, toll road payment, vehicle purchase or lease payment,vehicle repair service, vehicle maintenance service, or any combinationthereof.

In further non-limiting embodiments or aspects, the at least onepoint-of-sale terminal may include a plurality of point-of-saleterminals, the at least one geographic node of activity may include aplurality of geographic nodes of activity, and each geographic node ofactivity of the plurality of geographic nodes of activity may include asubset of point-of-sale terminals of the plurality of point-of-saleterminals. The region may include at least one channel of transit bywhich a traveler is able to travel to a point-of-sale terminal of the atleast one geographic node of activity. The threshold of trafficintensity may be predetermined and based at least partially on acapacity of traffic of the at least one channel of transit. The at leastone channel of transit may include a plurality of roads, and thecapacity of traffic may be based at least partially on a volume ofvehicular traffic capable of traveling along the plurality of roads.

In further non-limiting embodiments or aspects, the communication to theat least one navigation device may be relayed through at least onelocalized communication device positioned in a subregion of the region.The at least one localized communication device may be programmed and/orconfigured to communicate with navigation devices in the subregion tofacilitate generation of navigation routes for travelers in thesubregion. The at least one localized communication device may include aplurality of localized communication devices forming a subregionalcommunication network, and each localized communication device of theplurality of localized communication devices may be programmed and/orconfigured to communicate with navigation devices and other localizedcommunication devices to facilitate generation of navigation routes fortravelers in the subregion.

In further non-limiting embodiments or aspects, the at least one servercomputer may be programmed and/or configured to associate the at leastone geographic node of activity with a category of traffic intensity.The at least one server computer may also be programmed and/orconfigured to generate display data configured to cause the at least onenavigation device to display the at least one geographic node ofactivity as at least one point on a geographical map of the region. Avisual characteristic of the at least one point may represent thecategory of traffic intensity. The at least one message may include aplurality of transaction authorization requests in an electronic paymentprocessing network occurring in real-time. The at least one navigationdevice may be associated with at least one consumer who is travelingthrough the region via the navigation route.

According to one non-limiting embodiment or aspect, provided is acomputer program product for machine-learning-based traffic prediction.The computer program product includes at least one non-transitorycomputer-readable medium including program instructions that, whenexecuted by at least one processor, cause the at least one processor toreceive historic transaction data including a plurality of transactionsby at least one consumer. The program instructions also cause the atleast one processor to generate, using a machine-learning classificationmodel and based at least partially on the historic transaction data, atransportation categorization for the at least one consumerrepresentative of a mode of transportation to be taken by the at leastone consumer when the at least one consumer travels to complete atransaction at a point-of-sale terminal. The program instructionsfurther cause the at least one processor to receive at least one messageassociated with at least one transaction between the at least oneconsumer and at least one point-of-sale terminal in a region. Theprogram instructions further cause the at least one processor toidentify at least one geographic node of activity in the regionincluding the at least one point-of-sale terminal. The programinstructions further cause the at least one processor to generate, basedat least partially on the at least one message and the transportationcategorization of the at least one consumer, an estimate of trafficintensity for the at least one geographic node of activity. The estimateof traffic intensity is representative of a predicted volume of trafficfor a mode of transportation. The program instructions further cause theat least one processor to compare the estimate of traffic intensity forthe at least one geographic node of activity to a threshold of trafficintensity for the at least one geographic node of activity. The programinstructions further cause the at least one processor to, in response todetermining that the estimate of traffic intensity for the at least onegeographic node of activity satisfies the threshold: generate acommunication to at least one navigation device configured to cause theat least one navigation device to modify a navigation route; andcommunicate the communication to the at least one navigation device.

In further non-limiting embodiments or aspects, the generating of thetransportation categorization may include associating at least one typeof transaction with at least one mode of transportation. The at leastone mode of transportation may include at least transit by motorvehicle, and the at least one type of transaction may include at leastone of: gasoline purchase, toll road payment, vehicle purchase or leasepayment, vehicle repair service, vehicle maintenance service, or anycombination thereof.

In further non-limiting embodiments or aspects, the at least onepoint-of-sale terminal may include a plurality of point-of-saleterminals, the at least one geographic node of activity may include aplurality of geographic nodes of activity, and each geographic node ofactivity of the plurality of geographic nodes of activity may include asubset of point-of-sale terminals of the plurality of point-of-saleterminals. The region may include at least one channel of transit bywhich a traveler is able to travel to a point-of-sale terminal of the atleast one geographic node of activity. The threshold of trafficintensity may be predetermined and based at least partially on acapacity of traffic of the at least one channel of transit. The at leastone channel of transit may include a plurality of roads, and thecapacity of traffic may be based at least partially on a volume ofvehicular traffic capable of traveling along the plurality of roads.

In further non-limiting embodiments or aspects, the communication to theat least one navigation device may be relayed through at least onelocalized communication device positioned in a subregion of the region.The at least one localized communication device may be programmed and/orconfigured to communicate with navigation devices in the subregion tofacilitate generation of navigation routes for travelers in thesubregion. The at least one localized communication device may include aplurality of localized communication devices forming a subregionalcommunication network. Each localized communication device of theplurality of localized communication devices may be programmed and/orconfigured to communicate with navigation devices and other localizedcommunication devices to facilitate generation of navigation routes fortravelers in the subregion.

In further non-limiting embodiments or aspects, the program instructionsmay cause the at least one processor to associate the at least onegeographic node of activity with a category of traffic intensity. Theprogram instructions may also cause the at least one processor togenerate display data configured to cause the at least one navigationdevice to display the at least one geographic node of activity as atleast one point on a geographical map of the region. A visualcharacteristic of the at least one point may represent the category oftraffic intensity. The at least one message may include a plurality oftransaction authorization requests in an electronic payment processingnetwork occurring in real-time. The at least one navigation device maybe associated with at least one consumer who is traveling through theregion via the navigation route.

Additionally, generally provides is an improved system,computer-implemented method, and computer program product for a system,method, and computer program product for generating real-timepredictions for vehicle transportation requests using machine learning.

According to one non-limiting embodiment or aspect, provided is acomputer-implemented method for generating real-time predictions forvehicle transportation requests using machine learning, including:generating, with at least one processor and a machine-learningclassification model, a transportation categorization for each consumerof a plurality of consumers based at least partially on historictransaction data including a plurality of transactions by each of theplurality of consumers, each plurality of transactions for each consumerincluding a subset of transactions for vehicle transportation;processing, with at least one processor, a plurality of new transactionsby each consumer of the plurality of consumers, each new transaction ofthe plurality of new transactions associated with at least onegeographic node of activity; in response to processing the plurality ofnew transactions, generating, with at least one processor and themachine-learning classification model, a plurality of vehicletransportation predictions for the plurality of consumers, each vehicletransportation prediction based on the transportation categorization foreach consumer and a geographic node of activity associated with a newtransaction, each vehicle transportation prediction representing alikelihood that the consumer will request vehicle transportationsubsequent to conducting the new transaction; and generating, with atleast one processor, a supply map interface including at least onevisual identification of at least one location in which a number ofrequests for vehicle transportation is predicted to increase based onthe plurality of vehicle transportation predictions.

In further non-limiting embodiments or aspects, the transportationcategorization for each consumer may be generated based at leastpartially on a transaction type. The transaction type may be determinedbased on at least one of the following: a merchant category, a merchantidentity, a transaction amount, a transaction frequency, or anycombination thereof. The transportation categorization for each consumermay be generated based at least partially on condition data for theplurality of transactions, the condition data including at least one ofthe following: a time of day, a day of the week, a day of the year, aweather condition, a proximity to a specified location, a proximity to aspecified event, or any combination thereof.

In further non-limiting embodiments or aspects, the method may furtherinclude automatically dispatching, with at least one processor, at leastone vehicle to the at least one location. The geographic node ofactivity may correspond to a location of at least one point-of-saleterminal.

In further non-limiting embodiments or aspects, the method may furtherinclude receiving, by at least one processor, location data associatedwith a geographic location of at least one vehicle-for-hire; andassociating, by at least one processor, the geographic location of theat least one vehicle-for-hire with at least one geographic node ofactivity based on a proximity of the vehicle-for-hire to the at leastone geographic node of activity, wherein the at least one visualidentification of the at least one location in which a number ofrequests for vehicle transportation is predicted to increase is furthergenerated based on a number of vehicles-for-hire that is associated withthe at least one geographic node of activity corresponding to the atleast one location.

According to one non-limiting embodiment or aspect, provided is a systemfor generating real-time predictions for vehicle transportation requestsusing machine learning, including at least one processor programmedand/or configured to: generate, with a machine-learning classificationmodel, a transportation categorization for each consumer of a pluralityof consumers based at least partially on historic transaction dataincluding a plurality of transactions by each of the plurality ofconsumers, each plurality of transactions for each consumer including asubset of transactions for vehicle transportation; process a pluralityof new transactions by each consumer of the plurality of consumers, eachnew transaction of the plurality of new transactions associated with atleast one geographic node of activity; in response to processing theplurality of new transactions, generate, with machine-learningclassification model, a plurality of vehicle transportation predictionsfor the plurality of consumers, each vehicle transportation predictionbased on the transportation categorization for each consumer and ageographic node of activity associated with a new transaction, eachvehicle transportation prediction representing a likelihood that theconsumer will request vehicle transportation subsequent to conductingthe new transaction; and generate a supply map interface including atleast one visual identification of at least one location in which anumber of requests for vehicle transportation is predicted to increasebased on the plurality of vehicle transportation predictions.

In further non-limiting embodiments or aspects, the at least oneprocessor may be programmed and/or configured to generate thetransportation categorization for each consumer based at least partiallyon a transaction type. The at least one processor may be programmedand/or configured to determine the transaction type based on at leastone of the following: a merchant category, a merchant identity, atransaction amount, a transaction frequency, or any combination thereof.

In further non-limiting embodiments or aspects, the at least oneprocessor may be programmed and/or configured to generate thetransportation categorization for each consumer is based at leastpartially on condition data for the plurality of transactions, thecondition data including at least one of the following: a time of day, aday of the week, a day of the year, a weather condition, a proximity toa specified location, a proximity to a specified event, or anycombination thereof.

In further non-limiting embodiments or aspects, the at least oneprocessor may be further programmed and/or configured to automaticallydispatch at least one vehicle to the at least one location. Thegeographic node of activity may correspond to a location of at least onepoint-of-sale terminal.

In further non-limiting embodiments or aspects, the at least processormay be further programmed and/or configured to: receive location dataassociated with a geographic location of at least one vehicle-for-hire;and associate the geographic location of the at least onevehicle-for-hire with at least one geographic node of activity based ona proximity of the vehicle-for-hire to the at least one geographic nodeof activity, wherein the at least one visual identification of the atleast one location in which a number of requests for vehicletransportation is predicted to increase is further generated based on anumber of vehicles-for-hire that is associated with the at least onegeographic node of activity corresponding to the at least one location.

According to one non-limiting embodiment or aspect, provided is acomputer program product for generating real-time predictions forvehicle transportation requests using machine learning, includinginstructions, which, when executed by at least one processer, cause theat least one processor to: generate, with a machine-learningclassification model, a transportation categorization for each consumerof a plurality of consumers based at least partially on historictransaction data including a plurality of transactions by each of theplurality of consumers, each plurality of transactions for each consumerincluding a subset of transactions for vehicle transportation; process aplurality of new transactions by each consumer of the plurality ofconsumers, each new transaction of the plurality of new transactionsassociated with at least one geographic node of activity; in response toprocessing the plurality of new transactions, generate, withmachine-learning classification model, a plurality of vehicletransportation predictions for the plurality of consumers, each vehicletransportation prediction based on the transportation categorization foreach consumer and a geographic node of activity associated with a newtransaction, each vehicle transportation prediction representing alikelihood that the consumer will request vehicle transportationsubsequent to conducting the new transaction; and generate a supply mapinterface including at least one visual identification of at least onelocation in which a number of requests for vehicle transportation ispredicted to increase based on the plurality of vehicle transportationpredictions.

In further non-limiting embodiments or aspects, the computer programproduct may further include instructions, which, when executed by atleast one processer, cause the at least one processor to: generate thetransportation categorization for each consumer based at least partiallyon a transaction type, wherein the transaction type is determined basedon at least one of the following: a merchant category, a merchantidentity, a transaction amount, a transaction frequency, or anycombination thereof.

In further non-limiting embodiments or aspects, the computer programproduct may further include instructions, which, when executed by atleast one processer, cause the at least one processor to: generate thetransportation categorization for each consumer is based at leastpartially on condition data for the plurality of transactions, thecondition data including at least one of the following: a time of day, aday of the week, a day of the year, a weather condition, a proximity toa specified location, a proximity to a specified event, or anycombination thereof.

In further non-limiting embodiments or aspects, the computer programproduct may further include instructions, which, when executed by atleast one processer, cause the at least one processor to: toautomatically dispatch at least one vehicle to the at least onelocation. The geographic node of activity may correspond to a locationof at least one point-of-sale terminal.

In further non-limiting embodiments or aspects, the computer programproduct may further include instructions, which, when executed by atleast one processer, cause the at least one processor to: receivelocation data associated with a geographic location of at least onevehicle-for-hire; and associate the geographic location of the at leastone vehicle-for-hire with at least one geographic node of activity basedon a proximity of the vehicle-for-hire to the at least one geographicnode of activity, wherein the at least one visual identification of theat least one location in which a number of requests for vehicletransportation is predicted to increase is further generated based on anumber of vehicles-for-hire that is associated with the at least onegeographic node of activity corresponding to the at least one location.

Other preferred and non-limiting embodiments or aspects will be setforth in the following numbered clauses:

Clause 1: A computer-implemented method for generating real-timepredictions for vehicle transportation requests using machine learning,comprising: generating, with at least one processor and amachine-learning classification model, a transportation categorizationfor each consumer of a plurality of consumers based at least partiallyon historic transaction data comprising a plurality of transactions byeach of the plurality of consumers, each plurality of transactions foreach consumer including a subset of transactions for vehicletransportation; processing, with at least one processor, a plurality ofnew transactions by each consumer of the plurality of consumers, eachnew transaction of the plurality of new transactions associated with atleast one geographic node of activity; in response to processing theplurality of new transactions, generating, with at least one processorand the machine-learning classification model, a plurality of vehicletransportation predictions for the plurality of consumers, each vehicletransportation prediction based on the transportation categorization foreach consumer and a geographic node of activity associated with a newtransaction, each vehicle transportation prediction representing alikelihood that the consumer will request vehicle transportationsubsequent to conducting the new transaction; and generating, with atleast one processor, a supply map interface comprising at least onevisual identification of at least one location in which a number ofrequests for vehicle transportation is predicted to increase based onthe plurality of vehicle transportation predictions.

Clause 2: The computer-implemented method of clause 1, whereingenerating the transportation categorization for each consumer is basedat least partially on a transaction type.

Clause 3: The computer-implemented method of clauses 1 or 2, wherein thetransaction type is determined based on at least one of the following: amerchant category, a merchant identity, a transaction amount, atransaction frequency, or any combination thereof.

Clause 4: The computer-implemented method of any of clauses 1-3, whereingenerating the transportation categorization for each consumer is basedat least partially on condition data for the plurality of transactions,the condition data comprising at least one of the following: a time ofday, a day of the week, a day of the year, a weather condition, aproximity to a specified location, a proximity to a specified event, orany combination thereof.

Clause 5: The computer-implemented method of any of clauses 1-4, furthercomprising automatically dispatching, with at least one processor, atleast one vehicle to the at least one location.

Clause 6: The computer-implemented method of any of clauses 1-5, whereinthe geographic node of activity corresponds to a location of at leastone point-of-sale terminal.

Clause 7: The computer-implemented method of any of clauses 1-6, furthercomprising: receiving, by at least one processor, location dataassociated with a geographic location of at least one vehicle-for-hire;and associating, by at least one processor, the geographic location ofthe at least one vehicle-for-hire with at least one geographic node ofactivity based on a proximity of the vehicle-for-hire to the at leastone geographic node of activity, wherein the at least one visualidentification of the at least one location in which a number ofrequests for vehicle transportation is predicted to increase is furthergenerated based on a number of vehicles-for-hire that is associated withthe at least one geographic node of activity corresponding to the atleast one location.

Clause 8: A system for generating real-time predictions for vehicletransportation requests using machine learning, comprising at least oneprocessor programmed and/or configured to: generate, with amachine-learning classification model, a transportation categorizationfor each consumer of a plurality of consumers based at least partiallyon historic transaction data comprising a plurality of transactions byeach of the plurality of consumers, each plurality of transactions foreach consumer including a subset of transactions for vehicletransportation; process a plurality of new transactions by each consumerof the plurality of consumers, each new transaction of the plurality ofnew transactions associated with at least one geographic node ofactivity; in response to processing the plurality of new transactions,generate, with machine-learning classification model, a plurality ofvehicle transportation predictions for the plurality of consumers, eachvehicle transportation prediction based on the transportationcategorization for each consumer and a geographic node of activityassociated with a new transaction, each vehicle transportationprediction representing a likelihood that the consumer will requestvehicle transportation subsequent to conducting the new transaction; andgenerate a supply map interface comprising at least one visualidentification of at least one location in which a number of requestsfor vehicle transportation is predicted to increase based on theplurality of vehicle transportation predictions.

Clause 9: The system of clause 8, wherein the at least one processor isprogrammed and/or configured to generate the transportationcategorization for each consumer based at least partially on atransaction type.

Clause 10: The system of clauses 8 or 9, wherein the at least oneprocessor is programmed and/or configured to determine the transactiontype based on at least one of the following: a merchant category, amerchant identity, a transaction amount, a transaction frequency, or anycombination thereof.

Clause 11: The system of any of clauses 8-10, wherein the at least oneprocessor is programmed and/or configured to generate the transportationcategorization for each consumer is based at least partially oncondition data for the plurality of transactions, the condition datacomprising at least one of the following: a time of day, a day of theweek, a day of the year, a weather condition, a proximity to a specifiedlocation, a proximity to a specified event, or any combination thereof.

Clause 12: The system of any of clauses 8-11, wherein the at least oneprocessor is further programmed and/or configured to automaticallydispatch at least one vehicle to the at least one location.

Clause 13: The system of any of clauses 8-12, wherein the geographicnode of activity corresponds to a location of at least one point-of-saleterminal.

Clause 14: The system of any of clauses 8-13, wherein the at least oneprocessor is further programmed and/or configured to: receive locationdata associated with a geographic location of at least onevehicle-for-hire; and associate the geographic location of the at leastone vehicle-for-hire with at least one geographic node of activity basedon a proximity of the vehicle-for-hire to the at least one geographicnode of activity, wherein the at least one visual identification of theat least one location in which a number of requests for vehicletransportation is predicted to increase is further generated based on anumber of vehicles-for-hire that is associated with the at least onegeographic node of activity corresponding to the at least one location.

Clause 15: A computer program product for generating real-timepredictions for vehicle transportation requests using machine learning,comprising instructions, which, when executed by at least one processer,cause the at least one processor to: generate, with a machine-learningclassification model, a transportation categorization for each consumerof a plurality of consumers based at least partially on historictransaction data comprising a plurality of transactions by each of theplurality of consumers, each plurality of transactions for each consumerincluding a subset of transactions for vehicle transportation; process aplurality of new transactions by each consumer of the plurality ofconsumers, each new transaction of the plurality of new transactionsassociated with at least one geographic node of activity; in response toprocessing the plurality of new transactions, generate, withmachine-learning classification model, a plurality of vehicletransportation predictions for the plurality of consumers, each vehicletransportation prediction based on the transportation categorization foreach consumer and a geographic node of activity associated with a newtransaction, each vehicle transportation prediction representing alikelihood that the consumer will request vehicle transportationsubsequent to conducting the new transaction; and generate a supply mapinterface comprising at least one visual identification of at least onelocation in which a number of requests for vehicle transportation ispredicted to increase based on the plurality of vehicle transportationpredictions.

Clause 16: The computer program product of clause 15, further comprisinginstructions, which, when executed by at least one processer, cause theat least one processor to: generate the transportation categorizationfor each consumer based at least partially on a transaction type,wherein the transaction type is determined based on at least one of thefollowing: a merchant category, a merchant identity, a transactionamount, a transaction frequency, or any combination thereof.

Clause 17: The computer program product of any clauses 15 or 16, furthercomprising instructions, which, when executed by at least one processer,cause the at least one processor to: generate the transportationcategorization for each consumer is based at least partially oncondition data for the plurality of transactions, the condition datacomprising at least one of the following: a time of day, a day of theweek, a day of the year, a weather condition, a proximity to a specifiedlocation, a proximity to a specified event, or any combination thereof.

Clause 18: The computer program product of any of clauses 15-17, furthercomprising instructions, which, when executed by at least one processer,cause the at least one processor to automatically dispatch at least onevehicle to the at least one location.

Clause 19: The computer program product of any of clauses 15-18, whereinthe geographic node of activity corresponds to a location of at leastone point-of-sale terminal.

Clause 20: The computer program product of any of clauses 15-19, furthercomprising instructions, which, when executed by at least one processer,cause the at least one processor to: receive location data associatedwith a geographic location of at least one vehicle-for-hire; andassociate the geographic location of the at least one vehicle-for-hirewith at least one geographic node of activity based on a proximity ofthe vehicle-for-hire to the at least one geographic node of activity,wherein the at least one visual identification of the at least onelocation in which a number of requests for vehicle transportation ispredicted to increase is further generated based on a number ofvehicles-for-hire that is associated with the at least one geographicnode of activity corresponding to the at least one location.

These and other features and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structures and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description, and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of any limits. As used in thespecification and the claims, the singular forms of “a,” “an,” and “the”include plural referents unless the context clearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

Additional advantages and details of the non-limiting embodiments oraspects are explained in greater detail below with reference to theaccompanying figures, in which:

FIG. 1 is a schematic diagram of one non-limiting embodiment or aspectof a system and method for machine-learning-based traffic and/or vehicledemand prediction;

FIG. 2 is an illustrative diagram of one non-limiting embodiment oraspect of a system and method for machine-learning-based traffic and/orvehicle demand prediction;

FIG. 3 is an illustrative diagram of one non-limiting embodiment oraspect of a system and method for generating predictions formachine-learning-based traffic and/or vehicle transportation requestsusing machine learning;

FIG. 4 is an illustrative diagram of one non-limiting embodiment oraspect of a system and method for generating predictions formachine-learning-based traffic and/or vehicle transportation requestsusing machine learning;

FIG. 5A is an illustrative diagram of one non-limiting embodiment oraspect of a system and method for machine-learning-based trafficprediction;

FIG. 5B is an illustrative diagram of one non-limiting embodiment oraspect of a system and method for generating real-time predictions forvehicle transportation requests using machine learning;

FIG. 6A is an illustrative diagram of one non-limiting embodiment oraspect of a system and method for machine-learning-based trafficprediction;

FIG. 6B is an illustrative diagram of one non-limiting embodiment oraspect of a system and method for generating real-time predictions forvehicle transportation requests using machine learning;

FIG. 7 is an illustrative diagram of one non-limiting embodiment oraspect of a system and method for machine-learning-based trafficprediction;

FIG. 8 is a flow diagram of one non-limiting embodiment or aspect of asystem and method for machine-learning-based traffic prediction; and

FIG. 9 is a flow diagram of one non-limiting embodiment or aspect of asystem and method for generating real-time predictions for vehicletransportation requests using machine learning.

DETAILED DESCRIPTION

For purposes of the description hereinafter, the terms “upper,” “lower,”“right,” “left,” “vertical,” “horizontal,” “top,” “bottom,” “lateral,”“longitudinal,” and derivatives thereof shall relate to the disclosureas the disclosure is oriented in the drawing figures. However, it is tobe understood that the disclosure may assume various alternativevariations and step sequences, except where expressly specified to thecontrary. It is also to be understood that the specific devices andprocesses illustrated in the attached drawings, and described in thefollowing specification, are simply exemplary embodiments of thedisclosure. Hence, specific dimensions and other physicalcharacteristics related to the embodiments disclosed herein are not tobe considered as limiting. Also, it should be understood that anynumerical range recited herein is intended to include all sub-rangessubsumed therein. For example, a range of 1 to 10 is intended to includeall sub-ranges between (and including) the recited minimum value of 1and the recited maximum value of 10, that is, having a minimum valueequal to or greater than 1 and a maximum value of equal to or less than10.

As used herein, the terms “communication” and “communicate” refer to thereceipt or transfer of one or more signals, messages, commands, or othertype of data. For one unit (e.g., any device, system, or componentthereof) to be in communication with another unit means that the oneunit is able to directly or indirectly receive data from and/or transmitdata to the other unit. This may refer to a direct or indirectconnection that is wired and/or wireless in nature. Additionally, twounits may be in communication with each other even though the datatransmitted may be modified, processed, relayed, and/or routed betweenthe first and second unit. For example, a first unit may be incommunication with a second unit even though the first unit passivelyreceives data and does not actively transmit data to the second unit. Asanother example, a first unit may be in communication with a second unitif an intermediary unit processes data from one unit and transmitsprocessed data to the second unit.

As used herein, the term “transaction service provider” may refer to anentity that receives transaction authorization requests from merchantsor other entities and provides guarantees of payment, in some casesthrough an agreement between the transaction service provider and anissuer institution. The terms “transaction service provider” and“transaction service provider system” may also refer to one or morecomputer systems operated by or on behalf of a transaction serviceprovider, such as a transaction processing server executing one or moresoftware applications. A transaction processing server may include oneor more processors and, in some non-limiting embodiments, may beoperated by or on behalf of a transaction service provider.

As used herein, the term “issuer institution” may refer to one or moreentities, such as a bank, that provide accounts to customers forconducting payment transactions, such as initiating credit and/or debitpayments. For example, an issuer institution may provide an accountidentifier, such as a personal account number (PAN), to a customer thatuniquely identifies one or more accounts associated with that customer.The account identifier may be embodied on a physical financialinstrument, such as a payment card, and/or may be electronic and usedfor electronic payments. The terms “issuer institution,” “issuer bank,”and “issuer system” may also refer to one or more computer systemsoperated by or on behalf of an issuer institution, such as a servercomputer executing one or more software applications. For example, anissuer system may include one or more authorization servers forauthorizing a payment transaction.

As used herein, the term “account identifier” may include one or morePANs, tokens, or other identifiers associated with a customer account.The term “token” may refer to an identifier that is used as a substituteor replacement identifier for an original account identifier, such as aPAN. Account identifiers may be alphanumeric or any combination ofcharacters and/or symbols. Tokens may be associated with a PAN or otheroriginal account identifier in one or more databases such that thetokens can be used to conduct a transaction without directly using theoriginal account identifier. In some examples, an original accountidentifier, such as a PAN, may be associated with a plurality of tokensfor different individuals or purposes. An issuer institution may beassociated with a bank identification number (BIN) or other uniqueidentifier that uniquely identifies the issuer institution among otherissuer institutions.

As used herein, the term “merchant” may refer to an individual or entitythat provides goods and/or services, or access to goods and/or services,to customers based on a transaction, such as a payment transaction. Theterm “merchant” or “merchant system” may also refer to one or morecomputer systems operated by or on behalf of a merchant, such as aserver computer executing one or more software applications. The term“point-of-sale system” or “POS system”, as used herein, may refer to oneor more computers and/or peripheral devices used by a merchant to engagein payment transactions with customers, including one or more cardreaders, near-field communication (NFC) receivers, radio-frequencyidentification (RFID) receivers, and/or other contactless transceiversor receivers, contact-based receivers, payment terminals, computers,servers, input devices, and/or other like devices that can be used toinitiate a payment transaction.

As used herein, the term “mobile device” may refer to one or moreportable electronic devices configured to communicate with one or morenetworks. As an example, a mobile device may include a cellular phone(e.g., a smartphone or standard cellular phone), a portable computer(e.g., a tablet computer, a laptop computer, etc.), a wearable device(e.g., a watch, pair of glasses, lens, clothing, and/or the like), apersonal digital assistant (PDA), and/or other like devices. The term“client device,” as used herein, refers to any electronic device that isconfigured to communicate with one or more servers or remote devicesand/or systems. A client device may include a mobile device, anetwork-enabled appliance (e.g., a network-enabled television,refrigerator, thermostat, and/or the like), a computer, a POS system,and/or any other device or system capable of communicating with anetwork.

As used herein, the term “financial device” may refer to a portablepayment card (e.g., a credit or debit card), a gift card, a smartcard,smart media, a payroll card, a healthcare card, a wristband, amachine-readable medium containing account information, a keychaindevice or fob, an RFID transponder, a retailer discount or loyalty card,a mobile device executing an electronic wallet application, a personaldigital assistant, a security card, an access card, a wireless terminal,and/or a transponder, as examples. The financial device may include avolatile or a non-volatile memory to store information, such as anaccount identifier or a name of the account holder. The financial devicemay store account credentials locally on the device, in digital ornon-digital representation, or may facilitate accessing accountcredentials stored in a medium that is accessible by the financialdevice in a connected network.

As used herein, the term “server” may refer to or include one or moreprocessors or computers, storage devices, or similar computerarrangements that are operated by or facilitate communication andprocessing for multiple parties in a network environment, such as theinternet. In some non-limiting embodiments, communication may befacilitated over one or more public or private network environments andthat various other arrangements are possible. Further, multiplecomputers, e.g., servers, or other computerized devices, e.g., POSdevices, directly or indirectly communicating in the network environmentmay constitute a system, such as a merchant's POS system. Reference to aserver or a processor, as used herein, may refer to a previously-recitedserver and/or processor that is recited as performing a previous step orfunction, a different server and/or processor, and/or a combination ofservers and/or processors. For example, as used in the specification andthe claims, a first server and/or a first processor that is recited asperforming a first step or function may refer to the same or differentserver and/or a processor recited as performing a second step orfunction.

The term “account data,” as used herein, refers to any data concerningone or more accounts for one or more users. Account data may include,for example, one or more account identifiers, user identifiers,transaction histories, balances, credit limits, issuer institutionidentifiers, and/or the like.

In non-limiting embodiments or aspects of the present disclosure,consumer transaction data, both historic and real-time, may be used as apredictive proxy for directly tracking consumers traveling through aregion. It may be presumed that a consumer who completes a card presenttransaction with a merchant (e.g., physically presents a financialdevice to a merchant POS terminal at a merchant location) willthereafter leave the merchant and travel along one or more channels oftransit by one or more modes of transportation. That consumer enters theflow of traffic and increases the traffic in the area near the POSterminal by the consumer's physical presence in the network. In thismanner, ongoing transaction data coming from merchant POS terminals (thelocation of which are known) may be used to predict trends in trafficthat will occur subsequent to consumers completing purchases. Forexample, if there is a sudden spike in transaction activity at a clusterof POS terminals at a mall (e.g., one example geographic node ofactivity), then it may be inferred that, in a time period thereafter,the transportation traffic through or near that cluster of POS terminalswill increase as well. Conversely, if there is an absence of transactionactivity at one or more POS terminals in an area, then it may beinferred that traffic through or near that area will decrease. Thispremise may be leveraged to train machine-learning models to identifyhow consumers travel through a region (e.g., by what mode oftransportation) based on transactions conducted by the consumers, andwhen transactions occur, to estimate traffic intensity thereby.

In further non-limiting embodiments or aspects of the presentdisclosure, consumer transaction data, both historic and real-time, maybe used as a predictive proxy for predicting increases or decreases forvehicle-for-hire demand in specific locations within a region. It may bepresumed that a consumer who completes a card-present transaction with amerchant (e.g., physically presents a financial device to a merchant POSterminal at a merchant location) will thereafter leave the merchant andtravel using one or more modes of transportation. Moreover, a consumer'spast purchasing behavior (e.g., the frequency with which a user hasretained a vehicle-for-hire in the past) may be used to predict alikelihood that the consumer will attempt to engage a vehicle-for-hire(e.g., a ride-share vehicle, a taxi, a pedicab, a carriage, a rickshaw,a jitney, a rental car delivery service and/or the like) subsequent tocompleting the transaction. Further, the transaction type (e.g., asdetermined based on one or more factors such as merchant identity,merchant category, transaction amount, and/or the like) additionally oralternatively may be used to predict a likelihood that the consumer willattempt to engage a vehicle-for-hire subsequent to completing thetransaction. For example, it may be determined that consumers who haverecently completed transactions at alcohol-serving establishments arelikely to engage a vehicle-for-hire during an identified time intervalafter completing the transaction. Conversely, it may be determined thatconsumers who have recently completed transactions at an establishmentthat is located outside of normal walking distance from residentialareas or public transportation, or that generally sells large items thatare more likely to be transported using a personal vehicle (e.g., afurniture store or the like), are less likely to engage avehicle-for-hire during an identified time interval after completing thetransaction.

In addition, the predicted likelihood that the consumer will attempt toengage a vehicle-for-hire subsequent to completing the transaction maybe further determined based on one or more external conditions that maybe unrelated to the instant purchase or the consumer's past spendingbehavior. For instance, it may be determined that consumers may be morelikely to request a vehicle to hire and less likely use other modes oftransportation when certain weather conditions are present, duringcertain times of day, during certain days of the week, during certaindays of the year, or when a special event is occurring in the area. Forexample, in some instances it may be determined that consumers are morelikely to request a vehicle-for-hire and less likely to walk to publictransportation during instances of bad weather, during times of day whenhigher crime rates are generally experienced, on certain days of theweek (such as weekends), or on certain days of the year (e.g.,holidays). In non-limiting embodiments, the system described herein mayreceive condition data regarding one or more external conditions,including the foregoing examples, from one or more servers (e.g., viathe Internet) and such condition data may be further incorporated inorder to improve the relevant predictive models.

For example, after completing a card-present transaction, a consumer islikely to remain in the vicinity of the POS terminal where thetransaction was completed for a predictable or identifiable time period.The physical presence of more consumers in the vicinity of the POSterminal increases the potential demand for vehicles-for-hire in thearea surrounding that POS terminal, particularly if a number of theconsumers can be identified as likely to request a vehicle-for-hirebased on past purchasing behavior and/or the present transaction type.This trend may be predictive of spikes in demand for vehicles-for-hire,which might not otherwise be foreseeable. For example, if there is asudden spike in transaction activity at a cluster of POS terminals inthe vicinity of one or more alcohol-serving establishments (e.g., oneexample geographic node of activity), then it may be inferred that, in atime period thereafter, the demand for vehicles-for-hire in or near thatcluster of POS terminals will increase as well. Conversely, if there isan absence of transaction activity at one or more POS terminals in anarea, then it may be determined that demand for vehicles-for-hire in ornear that area will decrease. These trends may be leveraged to trainmachine-learning models to identify optimal travel locations and pathsfor a vehicle-for-hire to engage a new customer as quickly as possible.

In non-limiting embodiments or aspects of the present disclosure,described systems and methods improve over prior art systems byproviding a predictive traffic estimation system rather than a reactivetraffic estimation system, wherein predictions are further based onextrinsic parameters other than transit data itself. The describedsystems and methods may be used as standalone predictive systems, or incombination with other traffic models to augment the efficiency andaccuracy of computer-driven traffic prediction systems. Describedsystems and methods herein use machine-learning models to identify themode of transportation that a consumer may take based on the consumer'spurchase patterns. Based at least partly on the consumer's category oftransportation, the consumer's ongoing transactions may be used toextrapolate and predict regional traffic to and from points of sale,specific to those modes of transportation and even before the consumerhas reentered the transportation network. This leads to faster and moreaccurate traffic prediction times. For example, the system and methodsmay lead to faster and more accurate traffic prediction times that arefurther in advance of traffic events, because regional traffic may bepredicted based on where consumers will be and how the consumers willtravel through the region. Furthermore, traffic predictions are improvedby aggregating in real-time the activity of the individual consumers whoare transacting in the region, because a transaction processing servermay identify transactions occurring as transaction authorizationrequests are being processed. In addition, all computer systems andnetworks that rely on accurate and efficient traffic prediction (e.g.,emergency response networks, commuter navigation systems, public transitsystems, self-driving vehicle navigation systems, etc.) are improvedthereby.

In further non-limiting embodiments or aspects of the presentdisclosure, described systems and methods improve over prior art systemsby providing a predictive vehicle-for-hire demand estimation system,wherein predictions are based on extrinsic parameters other than currentlocation data provided via customer devices. The described systems andmethods may be used as standalone predictive systems or in combinationwith existing systems for connecting a vehicle-for-hire with a customerto improve the efficiency and accuracy of such systems.

Described systems and methods herein use machine-learning models toidentify the likelihood that a consumer will request a vehicle-for-hiresubsequent to engaging in a transaction based on the consumer's priorpurchase patterns and the circumstances surrounding the transaction.Based at least partly on the consumer's category of transportation, theconsumer's ongoing transactions may be used to extrapolate and predictincreases or decreases in the demand for vehicles-for-hire in thevicinity of points of sale, even before the consumer has reentered thetransportation network. This leads to faster and more accuratepredictions of demand for vehicles-for-hire. For example, the systemsand methods described herein may provide faster and more accuratepredictions of spikes in demand for vehicles-for-hire resulting fromspecial events that may not be publicly known, such as the conclusion ofa large social gathering where large numbers of consumers maysimultaneously exit an alcohol-serving establishment and requiretransportation. Furthermore, vehicle-for-hire demand predictions areimproved by aggregating in real-time the activity of the individualconsumers who are transacting in the region, because a transactionprocessing server may identify transactions occurring as transactionauthorization requests are being processed. In addition, all computersystems and networks that seek to connect vehicles-for-hire withcustomers (e.g., online portals for engaging a taxi or ride-sharevehicle) are improved thereby.

With specific reference to FIG. 1, and in non-limiting embodiments oraspects, provided is a system 100 for machine-learning-based trafficprediction. The system 100 includes one or more financial device holders(FDH) 102, also referred to herein as consumers, that each have one ormore financial devices 104 to complete transactions with merchants in aregion, via one or more merchant point-of-sale (POS) systems, e.g., oneor more POS terminals. In some non-limiting embodiments, the region mayinclude any geographic area in which exist one or more channels oftransit and one or more POS terminals 106. Each financial device 104 maybe associated with a transaction account for settlement of funds tocomplete transactions with merchants. Each transaction account may beassociated with one or more financial devices 104 and may be associatedwith one or more financial device holders 102, e.g., consumers. Thefinancial device 104 may be used to provide an account identifier for arespective transaction account for the completion of the transaction.Authorization requests for transactions between financial devices 104and merchant POS terminals 106 are received and processed by atransaction processing server 108, which may be controlled by atransaction service provider. The transaction processing server 108 iscommunicatively connected to a transaction database 110, to storetransaction data associated with the one or more transactions beingcompleted between financial devices 104 and merchant POS terminals 106,including, but not limited to: transaction amount, transaction time,transaction date, transaction type, merchant type, transaction location,account identifier, financial device holder identifier, transactiondescription, and/or the like.

With further reference to FIG. 1, and in further non-limitingembodiments, the system 100 includes a modeling server 112, which may bethe same server as the transaction processing server 108. The modelingserver 112 is configured to store and run a machine-learningclassification model to generate a transportation categorization foreach consumer, e.g., financial device holder 102. The transportationcategorization is representative of a most likely mode of transportationto be taken by the consumer when the consumer travels to complete atransaction in the subject region. For example, the transportationcategorization may include, but is not limited to: motor vehicle (e.g.,personal vehicle, hired vehicle, taxi, etc.), train (e.g., light rail,subway, etc.), bus, ferry, pedestrian, bicycle, and/or the like. Thetransportation categorization may be used to associate a consumer'stransactions with channels of transit corresponding to the consumer'slikely mode of transportation. For example, if consumer A is assigned atransportation categorization that includes transit by motor vehicle,consumer A's future transactions will indicate that roadway traffic willlikely increase in the time period subsequent to the transaction. Inanother example, if consumer B is assigned a transportationcategorization that includes rail transit, consumer B's futuretransactions will indicate that rail traffic will likely increase in thetime period subsequent to the transaction. In another example, ifconsumer C is assigned a transportation categorization that includeshired vehicles, consumer C's future transactions may indicate thatdemand for vehicles-for-hire will likely increase in the time periodsubsequent to the transaction.

With further reference to FIG. 1, and in further non-limitingembodiments, the machine-learning classification model may be trained todetermine a transportation categorization based on types of transactionsthat are indicative of corresponding modes of transportation. Forexample, a transportation categorization may include transit by motorvehicle, which may be assigned to consumers with types of transactionsincluding: gasoline purchase, toll road payment, vehicle purchase orlease payment, vehicle repair service, vehicle maintenance service,and/or the like. These indicators may be received in the transactiondata of authorization requests processed by a transaction processingserver 108, or received from a database of merchant data that indicatesthe respective merchant's type of business. In another example, atransportation categorization may include transit by rail, which may beassigned to consumers with types of transactions including: rail passpurchases, public transit cards, and/or the like. A consumer lackingtransaction types associated with one category of transit may also beassumed to belong to another category of transit. For example, if aconsumer has not made any gasoline purchases and has not made anypurchases at auto supply stores, auto service centers, or cardealerships, it may be inferred that the consumer does not own a motorvehicle and is therefore more likely to travel by a non-motor vehicle,unless by a vehicle-for-hire, such as a taxi, a ride-share vehicle,and/or the like. Additionally or alternatively, a consumer that hasregularly completed transactions with hired vehicle services in the pastmay be predicted to engage in further transactions with hired vehiclesunder similar conditions in the future.

With further reference to FIG. 1, and in further non-limitingembodiments, the machine-learning classification model may include anysuitable predictive classification model that may be trained on historictransactions to output classifications based on a current transactiondata input. For example, the machine-learning classification model mayinclude, but is not limited to: linear classifier models (e.g., logisticregression, naive Bayes classifier, etc.), support vector machinemodels, decision tree models, boosted tree models, random forest models,neural network models, nearest neighbor models, and/or the like. Themodeling server 112 may receive transaction data for model training orclassification by communicative connection to the transaction database110, either directly or indirectly via the transaction processing server108 or other server. Machine-learning classification model data may bestored in a model database 114 that is communicatively connected to themodeling server 112. The model database 114 may be the same database asthe transaction database 110. In further non-limiting embodiments, themodel may be a reinforcement learning-based solution (e.g., recurrentneural networks, Q-learning, etc.), which may classify consumers'transactions assigning positive or negative reinforcement scores. Theresults of such a reinforcement-based learning may be stored in themodel database 114 categorizing the consumer according to thetransportation mode. It will be appreciated that other configurationsare possible.

With further reference to FIG. 1, and in further non-limitingembodiments, the modeling server 112 may also be used to identify one ormore geographic nodes of activity in the region. A geographic node ofactivity may be a geographic area or point that is representative of thelocation of one or more merchant POS terminals 106 in a region. Forexample, a geographic node of activity may be a geographic area aroundand including one or more POS terminals. In a further example, ageographic node of activity may be a centroid of a cluster of one ormore POS terminals. The node may be a point located near or on a channelof transit, such as on a rail line, a rail station, a roadway, a roadintersection, a bus stop, a vehicle-for-hire pickup location (e.g., ataxi stand), and/or the like. A geographic node of activity may also bethe location of a POS terminal. To make such a determination, mappingdata may be stored in the model database 114 or another database, suchas a routing and communication database 118, and the locations of POSterminals may be used to identify one or more geographic nodes ofactivity. See FIGS. 2-7 for non-limiting illustrative diagramsrepresentative of a method including the step of identifying geographicnodes of activity.

The modeling server 112 may be used to generate, based at leastpartially on current transactions in the region (i.e., “newtransactions”) and/or the determined transportation categorization forthe consumers completing those transactions, an estimate of trafficintensity for the geographic node(s) of activity. The estimate oftraffic intensity is representative of a predicted volume of traffic fora given mode of transportation that passes through or near thegeographic node of activity. Increased numbers of consumer transactionsmay be considered to be proportional to increased traffic. The estimateof traffic intensity may be any quantitative or qualitative valuecapable of being compared to a predetermined threshold. In other words,the estimate of traffic intensity may be numerical (e.g., a percentile,a score, such as from 0 to 100 or 0 to 1, a rate of travelers per unittime, and/or the like) or value-related categorical (e.g.,low/medium/high, a star rating, a color intensity such asgreen/yellow/red, and/or the like). A numerical estimate of trafficintensity may be an absolute value, a score in a range, a rate, adifferential, and/or the like, and may be whole numbers, decimals,fractions, or any other quantitative representation.

Additionally, the modeling server 112 may be used to generate, based atleast partially on current transactions in the region and/or thedetermined transportation categorization for the consumers completingthose transactions, an estimate of the demand for a particular type oftransportation, such as a vehicle-for-hire, for the geographic node(s)of activity. The estimate of demand is representative of a predictednumber of transportation requests originating from within and/or nearthe geographic node of activity that will be initiated during anidentified time interval and may be represented as a vehicletransportation prediction. Increased numbers of card-presenttransactions, generally, or in identified merchant categories may beconsidered to be proportional to an increase in demand forvehicles-for-hire within and/or near the node. The estimate of demandmay be any quantitative or qualitative value capable of being comparedto a predetermined threshold and/or to like parameters of other nodes inthe region. In other words, the estimate of vehicle-for-hire demand maybe numerical (e.g., a percentile, a score, such as from 0 to 100 or 0 to1, a rate of travelers per unit time, and/or the like) or value-relatedcategorical (e.g., low/medium/high, a star rating, a color intensitysuch as green/yellow/red, and/or the like). A numerical estimate ofvehicle hire demand may be an absolute value, a score in a range, arate, a differential, and/or the like, and may be whole numbers,decimals, fractions, or any other quantitative representation.

With further reference to FIG. 1, and in further non-limitingembodiments, the modeling server 112 may compare the estimate of trafficintensity for the at least one geographic node of activity to athreshold of traffic intensity for the at least one geographic node ofactivity. The threshold of traffic intensity may be based on anyparameter or value indicative of an amount of traffic above or belowwhich would be desirable to reroute travelers in the region eithertoward or away from the geographic node of activity. For example, thethreshold of traffic intensity may be a lower bound and it would bedesirable to reroute traffic through or near the geographic node ofactivity because the estimate of traffic intensity is equal to and/orless than the threshold of traffic intensity. Alternatively, thethreshold of traffic intensity may be an upper bound and it would bedesirable to reroute traffic away from or around the geographic node ofactivity because the estimate of traffic intensity is equal to and/orgreater than the threshold of traffic intensity. The threshold oftraffic intensity may be predetermined and based at least partially on acapacity of traffic of one or more channels of transit that pass throughor near a geographic node of activity. A capacity of traffic may be arate of traffic and/or number of vehicles (of the evaluated mode oftransportation) that may travel through the channel of transit at adesired speed for regular traffic flow, whereby additional trafficbeyond the capacity of traffic would either reduce the safety and/orspeed of travelers in the channel of traffic. For example, if thegeographic node of activity is a geographic area, traffic capacities forall channels of transit passing through and/or near that geographic areamay be cumulated and set as an upper threshold of traffic intensity. Ina further example, if the geographic node of activity is a geographicpoint, traffic capacities for all channels of transit passing throughand/or near that geographic point may be cumulated and set as an upperthreshold of traffic intensity. Multiple thresholds may be employed incombination for the same geographic node of activity.

In some non-limiting embodiments, traffic intensity estimate andthreshold determination steps may be carried out by the modeling server112 or another server in the system 100. The cumulated traffic intensityat a current state may be periodically stored in a database, such as thecommunication database 118 or model database 114, and may be used in thefuture for determining state transitions. In some non-limitingembodiments, a state-based transition system (e.g., a Markov decisionengine) may be used to predict the likelihood of traffic evolution inthe network, according to the historic stored data. The data for such astate-based transition may be obtained from the communication database118, the model database 114, or any combination thereof. It will beappreciated that other configurations are possible.

Additionally, with continued reference to FIG. 1, and in furthernon-limiting embodiments, the modeling server 112 may compare theestimate of vehicle-for-hire demand for the at least one geographic nodeof activity to a threshold of vehicle-for-hire demand for the at leastone geographic node of activity and/or to the vehicle-for-hire demand ofother geographic notes of activity in the region. The threshold ofvehicle-for-hire demand may be based on any parameter or valueindicative of an amount of demand above or below which would bedesirable to reroute vehicles-for-hire, which are seeking to acquirecustomers either toward or away from the geographic node of activity.For example, the threshold of vehicle-for-hire demand may be a lowerbound and it would be desirable to reroute vehicles-for-hire away fromthe geographic node of activity because the supply of vehicles-for-hirein the vicinity of the geographic node of activity is predicted to begreater than or equal to the demand for vehicles-for-hire.

In non-limiting embodiments, the threshold of traffic intensity may bean upper bound and it may be desirable to reroute vehicles-for-hire toor near the geographic node of activity because the demand forvehicles-for-hire is predicted to greatly exceed the supply ofvehicles-for-hire in the vicinity of the geographic node. The thresholdof vehicle-for-hire demand may be predetermined and/or based at leastpartially on a determined or predicted number of vehicles-for-hire thatare likely to be seeking customers in the vicinity of the geographicnode during a given time period. For example, if the geographic node ofactivity is a geographic area, the number of vehicles-for-hire that areactively seeking customers in the vicinity of the geographic node may bedetermined based on navigation system data (e.g., GPS data) oruser-input location data received from transceivers associated with thevehicles-for-hire themselves and/or from a central dispatch location.Additionally or alternatively, the number of vehicles-for-hire that areactively seeking customers in the vicinity of the geographic node may bepredicted based on historical data (e.g., based on a number oftransactions involving vehicles-for-hire that took place during ananalogous time period in the past). Multiple thresholds may be employedin combination for the same geographic node of activity. In somenon-limiting embodiments, vehicle-for-hire demand estimate and thresholddetermination steps may be carried out by the modeling server 112 oranother server in the system 100. The cumulated vehicle-for-hire demandat a current state may be periodically stored in a database, such as thecommunication database 118 or model database 114, and may be used in thefuture for determining state transitions. In some non-limitingembodiments, a state-based transition system (e.g., a Markov decisionengine) may be used to predict the likelihood of vehicle-for-hire demandevolution in the network, according to the historic stored data. Thedata for such state-based transition may be obtained from thecommunication database 118, the model database 114, or any combinationthereof. It will be appreciated that other configurations are possible.

With further reference to FIG. 1, and in further non-limitingembodiments, the system 100 may include a routing and communicationserver 116 for responding to changes in estimated traffic intensityand/or vehicle-for-hire demand, including when estimated trafficintensity satisfies threshold traffic intensity and/or vehicle-for-hiredemand. Routing and communication may occur on the same server ordifferent servers. The routing and communication server 116 may be thesame server as the modeling server 112 and/or the transaction processingserver 108. The routing and communication server 116, in response to thedetermination that the estimate of traffic intensity and/or vehicle hiredemand for one or more geographic nodes of activity satisfy acorresponding threshold, may be triggered to take one or more actions.Those actions may include generating and communicating a communicationto one or more navigation devices 120 to modify navigation route(s) inthe subject region. A navigation device 120 may be a mobile device orother computing device moving with a traveler who is traveling throughthe region. A navigation device 120 may also be a server that computes anavigation route for a traveler and transmits the data to a computingdevice of the traveler. For example, a navigation device 120 may be asmartphone running a mapping application. In another example, anavigation device 120 may be a mapping server of a navigation servicethat sends and receives data to a user via a mobile device. A navigationdevice 120 may generate one or more navigation routes for one or moretravelers and/or vehicle-for-hire operators. In non-limitingembodiments, the generated communication may be sent by the routing andcommunication server 116 to the navigation device 120 to modify anavigation route, e.g., change at least one segment of a navigationroute of a traveler and/or vehicle-for-hire operator. A segment of anavigation route may be modified such that the navigation route isre-routed closer to or farther away from one or more geographic nodes ofactivity. For instance, a navigation route may be modified to passthrough or near geographic nodes of activity with lower estimates oftraffic intensity, and the navigation route may be modified to not passthrough and/or avoid geographic nodes of activity with higher estimatesof traffic intensity. For instance, a navigation route for avehicle-for-hire may be modified to pass through or near geographicnodes of activity where the demand for vehicles-for-hire is predicted toexceed the supply thereof. There may be a number of navigation devices120 in a region at one point in time, and a number of navigation routesof said navigation devices 120 may be modified in response tocommunications from the routing and communication server 116.

With further reference to FIG. 1, and in further non-limitingembodiments, the communication(s) configured to modify one or morenavigation routes of one or more navigation devices 120 may becommunicated to navigation devices 120 via one or more localizedcommunication devices 122 in the region. A localized communicationdevice 122 may be any communication device, e.g., a transceiver, a localnetwork relay node, etc., configured to receive the communication fromthe routing and communication server 116 and send the communication to anavigation device 120. A localized communication device 122 may beassociated with a specific subregion of the region, in which thelocalized communication device 122 communicated with navigation devices120 traveling through the subregion, e.g., a geographic portion of thesubject region that is less than or equal to the area of the region. Asubregion may be associated with one or more localized communicationdevices 122, and a localized communication device 122 may be associatedwith one or more subregions. Each localized communication device 122 maybe configured to communicate with other localized communication devices122, in addition to navigation devices 120 and the routing andcommunication server. Localized communication devices 122 may also beassociated with merchants and integrated with merchant computingdevices. Localized communication devices 122 may also correspond to, andbe positioned in, at, or near, geographic nodes of activity. Localizedcommunication devices 122 may communicate with the routing andcommunication server 116 in real-time as transactions in the region arebeing processed by the transaction processing server 108 and are beingused to compute estimates of traffic intensity and/or vehicle-for-hiredemand by the modeling server 112.

With specific reference to FIG. 2, and in non-limiting embodiments oraspects, provided is an illustrative diagram of a system and method formachine-learning-based traffic prediction and/or vehicle-for-hire demandprediction. Depicted is region 200 that includes various channels oftransit, including a road network 202, a number of rail stations (S)204, and a railway 206. In some non-limiting embodiments, region 200 mayinclude other channels of transit not explicitly depicted herein,including waterways, sidewalks, and the like. The region 200 furtherincludes merchant POS terminals (P) 208 throughout the region, withwhich consumers, e.g., financial device holders, may completetransactions. The POS terminals 208 may be communicatively connected toa transaction processing server to receive and process the transactiondata associated with purchases made by consumers in the region 200. Thetransaction processing server may or may not be physically located inthe region 200. The transaction data may be used to generatetransportation categorizations for consumers that are representative ofa mode of transportation to be taken by the respective consumer whentraveling to complete a transaction in the region 200. The transactiondata may also be used to generate estimates of traffic intensity and/orvehicle-for-hire demand, discussed further below.

With specific reference to FIG. 3, and in non-limiting embodiments oraspects, provided is a further illustrative diagram of a system andmethod for machine-learning-based traffic prediction and/orvehicle-for-hire demand prediction. As depicted, the POS terminals 208in the region 200 may be grouped into clusters of transaction activity209 in the region 200. A cluster of transaction activity 209 may includeone or more POS terminals 208, and a cluster 209 may overlap and/orinclude one or more of the same POS terminals 208 as another cluster209.

With specific reference to FIG. 4, and in non-limiting embodiments oraspects, provided is a further illustrative diagram of a system andmethod for machine-learning-based traffic prediction and/orvehicle-for-hire demand prediction. Geographic nodes of activity 210(shown with the label “N” in FIGS. 4-7) may be identified to representgroups of one or more POS terminals 208 in the region 200. In thenon-limiting embodiment depicted, the geographic node of activity 210 isdetermined in a position on a channel of transit and within an area of acorresponding cluster of transaction activity 209. Geographic nodes ofactivity 210 may also be positioned on intersections in channels oftransit, as depicted in the non-limiting exemplary embodiment. In somenon-limiting embodiments, clusters of transaction activity 209 need notbe generated prior to the identification of geographic nodes ofactivity, and a geographic node of activity 210 may correspond directlywith the location of one or more POS terminals 208. Geographic nodes ofactivity 210 may also be automatically determined by calculating thecentroid of a cluster of transaction activity 209. It will beappreciated that many configurations are possible.

With specific reference to FIG. 5A, and in non-limiting embodiments oraspects, provided is a further illustrative diagram of a system andmethod for machine-learning-based traffic prediction. A modeling servermay generate an estimate of traffic intensity 214 for each geographicnode of activity 210 in the region 200. In the non-limiting exampledepiction, the estimates of traffic intensity 214 are shown ascategorical ratings of traffic intensity, including labels such as low,medium, and high. It will be appreciated that these labels are forillustration, and any of the previously described estimate types,including numerical scores, may be used for a geographic node ofactivity 210. Also within the region 200 are any number of travelers,and a traveler may use a navigation device to determine an initialnavigation route 212 through the region. In the non-limiting embodimentof FIG. 5A, the depicted initial navigation route 212 is for an exampletraveler that is traveling by motor vehicle from the lower-left to theupper-right of the region 200. It will be appreciated that the stepsherein may be carried out for other modes of transportation and channelsof transit. The non-limiting example depicted initial navigation route212 also passes through some of the geographic nodes of activity 210 inthe region 200. It will be appreciated that navigation routes 212 maypass through geographic nodes of activity 210 if geographic nodes ofactivity 210 are points positioned on channels of transit or areasincluding channels of transit. It will be appreciated that navigationroutes 212 may also include segments adjacent to, but not passingthrough, geographic nodes of activity 210.

With further reference to FIG. 5A, and in further non-limitingembodiments, a modeling server may compare estimates of trafficintensity 214 to thresholds of traffic intensity for respective nodes210 in the region 200. If an estimate 214 satisfies a threshold, aserver may trigger an action to modify one or more navigation routes 212in the region 200. For example, the action may be to generate andcommunicate a communication to one or more navigation devices to modifyan initial navigation route 212 to avoid nodes 210 with high trafficintensity (e.g., nodes having estimates of traffic intensity satisfyingupper bound thresholds), and/or pass near or through nodes 210 with lowtraffic intensity (e.g., nodes having estimates of traffic intensitysatisfying lower bound thresholds). In the non-limiting exampledepiction, two nodes 210 have low estimates of traffic intensity 214,one node 210 has a medium estimate of traffic intensity 214, and onenode 210 has a high estimate of traffic intensity 214. A navigationdevice of a traveler in the region 200 may modify the initial navigationroute 212 to one or more new navigation routes 216, 218. A newnavigation route 216, 218 may be suggested to a traveler for selection,or the initial navigation route 212 may be automatically modified by thenavigation device. New navigation routes 216, 218 may also be generatedvariably based on how much an estimate of traffic intensity 214surpasses a threshold. For example, specifically concerning the node 210with a high estimate of traffic intensity 214, a first new navigationroute 216 may be suggested/generated if the threshold is met orsurpassed by a certain amount, whereas a second new navigation route 218may be suggested/generated if the threshold is met or surpassed by agreater amount. In both cases, the initial navigation route 212 ismodified to avoid areas of high traffic intensity.

With specific reference to FIG. 5B, and in non-limiting embodiments oraspects, provided is a further illustrative diagram of a system andmethod for generating real-time predictions for vehicle transportationrequests using machine learning. A modeling server may generate anestimate of predicted vehicle-for-hire demand 254 for each geographicnode of activity 260 in the region 250. In the non-limiting exampledepicted in FIG. 5B, the estimates of predicted vehicle-for-hire demand254 are shown as categorical ratings of predicted demand, includinglabels such as low, medium, and high. It will be appreciated that theselabels are for illustration purposes only, and that any of thepreviously described estimate types, including numerical scores, may beused for a geographic node of activity 260. Also within the region 250are any number of vehicles-for-hire, and a vehicle-for-hire may use anavigation device to determine an initial navigation route 252 throughthe region when seeking to acquire a customer. In the non-limitingembodiment shown in FIG. 5B, the depicted initial navigation route 252is for an example vehicle-for-hire such as a taxi cab or a vehicleparticipating in a ride-share program that is seeking to acquire acustomer in the region 250. It will be appreciated that the steps hereinmay be carried out for other modes of transportation and channels oftransit. The non-limiting example of the initial navigation route 252depicted in FIG. 5B also passes through some of the geographic nodes ofactivity 260 in the region 250. It will be appreciated that navigationroutes 252 may pass through geographic nodes of activity 260 ifgeographic nodes of activity 260 are points positioned on channels oftransit or areas including channels of transit. It will be appreciatedthat navigation routes 252 may also include segments adjacent to, butnot passing through, geographic nodes of activity 260.

With further reference to FIG. 5B, and in non-limiting embodiments oraspects, a modeling server may compare estimates of predictedvehicle-for-hire demand 254 to thresholds of predictive vehicle-for-hiredemand for respective nodes 260 in the region 250. If an estimate 254satisfies a threshold (e.g., meets or exceeds the threshold value), aserver may trigger an action to modify one or more navigation routes 252in the region 250. For example, the action may be to generate andcommunicate a communication to one or more navigation devices to modifyan initial navigation route 252 to steer toward nodes 260 with highpredicted vehicle-for-hire demand (e.g., nodes having predictedvehicle-for-hire demands exceeding lower bound thresholds), and/or toavoid nodes 260 with low predicted vehicle-for-hire demand (e.g., nodeshaving predicted vehicle-for-hire demands falling below lower boundthresholds). In the non-limiting example depicted in FIG. 5B, two nodes260 have low predicted vehicle-for-hire demand 254, one node 260 has amedium predicted vehicle-for-hire demand 254, and one node 260 has ahigh predicted vehicle-for-hire demand 254. A navigation device of avehicle-for-hire in the region 250 may modify the initial navigationroute 252 to one or more new navigation routes 256, 258. A newnavigation route 256, 258 may be suggested to a vehicle-for-hireoperator for selection, or the initial navigation route 252 may beautomatically modified by the navigation device. New navigation routes256, 258 may also be generated variably based on how much an estimate oftraffic intensity 254 surpasses a threshold. For example, specificallyconcerning the node 260 with a high predicted vehicle-for-hire demand254, a first new navigation route 256 may be suggested or generatedautomatically if the threshold is met or surpassed by a certain amount,whereas a selection among a first new navigation route 256 and a secondnew navigation route 258 may be suggested or generated if the thresholdis met or surpassed by a lesser amount. In both cases, the initialnavigation route 252 is modified to steer toward nodes having a higherpredicted vehicle-for-hire demand, however, in the latter case, when thedifference in predicted vehicle-for-hire demand is not as significant,the vehicle operator is provided with a greater number of choices toallow for selection among potential routes based on other factors suchas vehicle operator preference.

With specific reference to FIG. 6A, and in non-limiting embodiments oraspects, provided is a further illustrative diagram of a system andmethod for machine-learning-based traffic prediction. The region 200includes one or more localized communication devices 222 that areconfigured to communicate with a routing and communication server, otherlocalized communication devices 222, and travelers 220 in the region200. The localized communication devices 222 may be configured tocommunicate with other devices 222 and travelers 220 within an effectiveradius or area of the region 200, also referred to herein as asubregion. Travelers 220 may have navigation devices traveling with themin the region 200, by which travelers 220 are navigating and that areupdated via nearby localized communication devices 222. Travelers 220may also have personal communication devices that receive routinginformation from a remote navigation device via nearby localizedcommunication devices 222. The localized communication devices 222 mayalso directly correspond to or approximate the position of one or moregeographic nodes of activity 210. Localized communication devices 222may communicate the local traffic intensity of surrounding points,areas, or channels of transit to other localized communication devices222 to more directly coordinate the flow of traffic through the region200. As shown, a localized communication device 222 is in communicationwith the traveler 220, and the initial navigation route 212 through theregion 200 will be modified by the system to either a first newnavigation route 216 or a second new navigation route 218. It will beappreciated that any number of new navigation routes 216, 218 may begenerated and/or communicated to a traveler's 220 navigation device, inorder to avoid nodes 210 of high traffic intensity or seek out nodes 210of low traffic intensity.

With specific reference to FIG. 6B, and in non-limiting embodiments oraspects, provided is a further illustrative diagram of a system andmethod for machine-learning-based traffic prediction. The region 250includes one or more localized communication devices 262 that areconfigured to communicate with a routing and communication server, otherlocalized communication devices 262, and vehicles-for-hire 270 in theregion 250. The localized communication devices 262 may be configured tocommunicate with other devices 262 and vehicles-for-hire 270 within aneffective radius or area of the region 250, also referred to herein as asubregion. Vehicles-for-hire 270 may have navigation devices travelingwith them in the region 250, by which the vehicles-for-hire 270 arenavigating and that are updated via nearby localized communicationdevices 262. Vehicles-for-hire 270 may also have personal communicationdevices that receive routing information from a remote navigation devicevia nearby localized communication devices 262. The localizedcommunication devices 262 may also directly correspond to or approximatethe position of one or more geographic nodes of activity 260. Localizedcommunication devices 262 may communicate the predicted vehicle-for-hiredemand of surrounding points, areas, or channels of transit to otherlocalized communication devices 262 to more directly coordinate the flowof multiple vehicles-for-hire within the region 250 to appropriategeographic nodes of activity 260. As shown, a localized communicationdevice 262 is in communication with the vehicle-for-hire 270, and theinitial navigation route 252 through the region 250 to acquire acustomer will be modified by the system to either a first new navigationroute 256 or a second new navigation route 258. It will be appreciatedthat any number of new navigation routes 256, 258 may be generatedand/or communicated to a vehicle-for-hire's 270 navigation device inorder to steer toward nodes 260 with a high predicted vehicle-for-hiredemand and to avoid nodes 260 of low predicted vehicle-for-hire demand.

With specific reference to FIG. 7, and in non-limiting embodiments oraspects, provided is a further illustrative diagram of a system andmethod for machine-learning-based traffic prediction. In the depictednon-limiting embodiment, shown is an initial navigation route 230 fromthe lower-left of the region 200 to the upper-right of the region 200that combines two modes of transportation, namely, pedestrian andrailway transit. It will be appreciated that one or more modes oftransportation may be combined and accounted for in the systems andmethods described herein. In the example shown, the initial navigationroute 230 is a pedestrian route that extends to a first railway station204, proceeds to the next railway station 204, and resumes again by apedestrian route to the top of the region 200. This initial navigationroute 230 happens to pass through two geographic nodes of activity 210,but it will be appreciated that the steps described herein may also beapplied to routes passing near or around geographic nodes of activity.In the depicted example, there is a node 210 with a high estimate oftraffic intensity 214, which may meet or exceed an upper-bound thresholdof traffic intensity for that node 210. In response, a routing andcommunication server may communicate with a navigation device of atraveler to modify the navigation route 230 to a new navigation route232 that travels less proximal to the node 210 having a high estimate oftraffic intensity 214. As shown in the example depicted, the newnavigation route 232 instead proceeds to a third railway station 204further to the right of the region 200 to avoid passing through the node210 with a high estimate of traffic intensity 214. It will beappreciated that the same techniques may be applied to optimize a routethat may or may not pass directly through nodes of activity, which mayinstead be modified to proceed closer to nodes 210 with less traffic andfarther from nodes 210 with more traffic.

With specific reference to FIG. 8, and in non-limiting embodiments oraspects, provided is a method 300 for machine-learning-based trafficprediction. The method 300 may be executed by one or more servers, eachincluding one or more computer processors. Data that is communicatedwith the one or more servers may be stored in one or more databases thatare communicatively connected to the one or more servers. The method 300includes, in step 302, receiving historic transaction data including anumber of transactions by one or more consumers in a region. The term“historic transaction data” may include transaction data fortransactions between consumers and merchants completed prior to andincluding up to the moment of traffic estimation for nodes in theregion. In step 304, using a machine-learning classification model andbased at least partially on the historic transaction data, atransportation categorization is generated for one or more consumers.The transportation categorization is representative of a mode oftransportation to be taken by the corresponding consumer when theconsumer travels to complete a transaction at a point-of-sale terminalin the region. Transportation categorization may include, but is notlimited to: motor vehicle (e.g., personal vehicle, hired vehicle, taxi,etc.), train (e.g., light rail, subway, etc.), bus, ferry, pedestrian,bicycle, and/or the like. The transportation categorization may begenerated by, in step 305, associating consumer transactions with modesof transportation. As described above, merchant business types andtransaction types may be indicators of a mode of transportation aconsumer may use.

With further reference to FIG. 8, and in further non-limitingembodiments, the method 300 includes, in step 306, receiving one or moremessages associated with one or more transactions between consumers andPOS terminals in the region. The message may include: an authorizationrequest to be processed by a transaction service provider, an encryptedcommunication containing transaction data that is transmitted to amodeling server from a consumer device or a POS terminal, a transactionreceipt forwarded from a consumer or a merchant, and/or the like. Itwill be appreciated that messages associated with transactions beingcompleted may be embodied by many forms of communication. In apreferred, non-limiting embodiment, the messages are transactionauthorization requests that are processed in real-time by a transactionprocessing server of a transaction service provider, as in step 307. Instep 308, one or more geographic nodes of activity are identified in theregion. A geographic node of activity may include one or more POSterminals, such as a cluster of POS terminals having the node as acentroid or point therein, or may correspond directly to a POS terminal,as in step 309. In step 310, one or more estimates of traffic intensityare generated for corresponding one or more geographic nodes ofactivity. An estimate of traffic intensity is representative of apredicted volume of traffic for a mode of transportation. As describedabove, an estimate of traffic intensity may be quantitative orqualitative such that it may be compared to a threshold trafficintensity. In step 312, one or more estimates of traffic intensity arecompared to corresponding one or more thresholds of traffic intensity. Athreshold of traffic intensity may be determined by the traffic capacityof the channel(s) of transit by which a traveler may travel to anassociated geographic node of activity, as in step 313. A threshold oftraffic intensity may be an upper bound, which when satisfied indicatesa higher amount of traffic (indicating the node should be avoided), or alower bound, which when satisfied indicates a lower amount of traffic(indicating the node need not be avoided, or may be actively sought totravel through).

With further reference to FIG. 8, and in further non-limitingembodiments, the method 300 includes, in step 314, in response to one ormore estimates of traffic intensity satisfying corresponding one or morethresholds of traffic intensity, generating and communicating acommunication to one or more navigation devices, the communicationconfigured to cause one or more navigation devices to modify anavigation route through the region. A navigation route of a targetnavigation device may be modified to avoid traveling through or nearnodes of higher estimates of traffic intensity and/or seek travelingthrough or near nodes of lower estimates of traffic intensity.Communications to navigation devices may be transmitted through anysuitable communication network, or more particularly, through a networkof localized communication devices in the region that are configured tocommunicate with each other and/or communication devices of travelers inthe region, in step 315. Localized communication devices may beassociated with one or more subregions, and subregions may be associatedwith one or more localized communication device.

Step 314, in which a communication is sent to a navigation device, mayinclude or be parallel to a step of generating display data configuredto cause the navigation device to the geographic node of activity as apoint on a geographical map of the region, wherein a visualcharacteristic of the point represents the category of trafficintensity. The visual characteristic may be a shape, color, icon,movement, animation, brightness, and/or the like that is configured toconvey a traffic intensity. For example, the navigation device mayreceive communication display data configured to show the node as acircular dot that is colored according to traffic intensity, where redindicates high traffic intensity, yellow indicates moderate trafficintensity, and green indicates low traffic intensity. Alternatively,nodes having estimates of traffic intensity surpassing an upper-boundthreshold may be visually represented as a point with a warning icon,such as an exclamation mark. Furthermore, the navigation devices may bephysically moving with travelers as the travelers are traveling throughthe region, or the navigation devices may be positioned remotely fromthe travelers and communicate the navigation route to the traveler via aseparate traveler communication device.

With specific reference to FIG. 9, and in non-limiting embodiments oraspects, provided is a method 400 for generating real-time predictionsfor vehicle transportation requests using machine learning. The method400 may be executed by one or more servers, each including one or morecomputer processors. Data that is communicated with the one or moreservers may be stored in one or more databases that are communicativelyconnected to the one or more servers. The method 400 includes, in step402, receiving historic transaction data including a number oftransactions by one or more consumers in a region. In accordance withnon-limiting embodiments or aspects, the term “historic transactiondata” may include transaction data for transactions between consumersand merchants completed prior to and including up to the moment ofpredicted vehicle demand estimation for nodes in the region. Innon-limiting embodiments, the historic transaction data may specificallyinclude a subset of transactions for the consumer that involvetransportation requests generally and/or for specific types oftransportation (e.g., vehicles-for-hire). In step 404, using amachine-learning classification model and based at least partially onthe historic transaction data, a transportation categorization isgenerated for one or more consumers. In accordance with thisnon-limiting embodiment or aspect, the transportation categorization maybe representative of a likelihood that the corresponding consumer is torequest a specified type of vehicle-for-hire (e.g., a ride-sharevehicle, a taxi, a pedicab, a carriage, a rickshaw, a jitney, a rentalcar delivery service and/or the like) subsequent to (e.g., within anidentified time interval after) completing a card-present transaction.The transportation categorization may be generated by, in step 405,associating consumer transactions with modes of transportation. Asdescribed above, merchant business types and transaction types may beindicators of a mode of transportation a consumer may be likely toutilize (e.g., a vehicle-for-hire) within an identified time interval(e.g., a time interval corresponding to a predicted travel time for thevehicle-for-hire to reach the geographic node of activity).

With further reference to FIG. 9, and in further non-limitingembodiments, the method 400 includes, in step 406, receiving one or moremessages associated with one or more transactions between consumers andPOS terminals in the region. The message may include: an authorizationrequest to be processed by a transaction service provider, an encryptedcommunication containing transaction data that is transmitted to amodeling server from a consumer device or a POS terminal, a transactionreceipt forwarded from a consumer or a merchant, and/or the like. Itwill be appreciated that messages associated with transactions beingcompleted may be embodied by many forms of communication. In apreferred, non-limiting embodiment, the messages are transactionauthorization requests that are processed in real-time by a transactionprocessing server of a transaction service provider, as in step 407. Instep 408, one or more geographic nodes of activity are identified in theregion. A geographic node of activity may include one or more POSterminals, such as a cluster of POS terminals having the node as acentroid or point therein, or may correspond directly to a POS terminal,as in step 409. In step 410, one or more estimates of vehicle-for-hiredemand are generated and are identified as corresponding one or moregeographic nodes of activity. An estimate of vehicle demand isrepresentative of a predicted number of consumers who are likely torequest a vehicle-for-hire during an identified time interval. Asdescribed above, an estimate of vehicle-for-hire demand may bequantitative, qualitative, and/or comparative such that it may becompared to a threshold vehicle demand and/or to the estimated vehicledemand of other geographic nodes in the region.

In step 412, one or more estimates of vehicle-for-hire demand arecompared to corresponding one or more thresholds of vehicle-for-hiredemand. A threshold of vehicle-for-hire demand may be determined by apredicted wait time a vehicle-for-hire located in the vicinity of thegeographic node would experience before engaging a new customer. Athreshold of vehicle demand may be a lower bound, which when exceeded,indicates a sufficiently high amount of vehicle-for-hire demand(indicating the node is a desirable location for a vehicle-for-hire toacquire new customers), or an high demand threshold, which whensatisfied indicates an abnormally high degree of vehicle-for-hire demand(indicating the node should be actively traveled to in order to acquirenew customers immediately). In step 413, one or more thresholds may beadjusted based on a transportation supply estimate for the geographicnode representative of a number of vehicles-for-hire that are activelyseeking to acquire customers in the vicinity of the geographic node. Thetransportation supply estimate may be determined by vehicle operatorinput data and/or navigation system (e.g., GPS) data fromvehicles-for-hire in communication with the system. For example, avehicle demand threshold may be increased by a predetermined amount foreach vehicle-for-hire that is known or predicted to be actively seekingto acquire customers in the vicinity of the geographic node, such that avehicle-for-hire is more likely directed away from geographic nodeswhere a large supply of vehicles-for-hire is already present and lesslikely to be directed toward geographic nodes where a smaller supply ofvehicles-for-hire is present.

With further reference to FIG. 9, and in non-limiting embodiments oraspects, the method 400 includes, in step 414, in response to one ormore estimates of vehicle-for-hire demand satisfying corresponding oneor more thresholds of traffic intensity, generating and communicating acommunication to one or more navigation devices, the communicationconfigured to cause one or more navigation devices to modify anavigation route through the region or to automatically dispatch avehicle-for-hire to a specified location in the region. A navigationroute of a target navigation device may be modified to steer towardgeographic nodes with higher estimates of traffic intensity and/or avoidgeographic nodes having lower estimates of traffic intensity.Communications to navigation devices may be transmitted through anysuitable communication network, or more particularly, through a networkof localized communication devices in the region that are configured tocommunicate with each other and/or communication devices of travelers inthe region, in step 415. Localized communication devices may beassociated with one or more subregions, and subregions may be associatedwith one or more localized communication device.

Still referring to FIG. 9, step 414, in which a communication is sent toa navigation device, may include or be performed concurrently with astep of generating display data configured to cause the navigationdevice to the geographic node of activity as a point on a geographicalmap of the region, wherein a visual characteristic of the pointrepresents the category of vehicle-for-hire demand level. The visualcharacteristic may be a shape, color, icon, movement, animation,brightness, and/or the like that is configured to convey avehicle-for-hire demand level. For example, the navigation device mayreceive communication display data configured to show the node as acircular dot that is colored according to vehicle-for-hire demand level,where red indicates low vehicle-for-hire demand, yellow indicatesmoderate vehicle-for-hire demand, and green indicates highvehicle-for-hire demand. Alternatively, nodes having estimates ofvehicle-for-hire demand surpassing an upper-bound threshold may bevisually represented as a point with an icon indicating that thelocation is a particularly desirable place to acquire a new customer.Furthermore, the navigation devices may be physically moving withvehicles-for-hire as the vehicles-for-hire are traveling through theregion, or the navigation devices may be positioned remotely from thevehicles-for-hire (e.g., at a central dispatch location) and communicatethe navigation route to the vehicle-for-hire via a separate travelercommunication device.

Although the disclosed system, method, and computer program product hasbeen described in detail for the purpose of illustration based on whatis currently considered to be the most practical and non-limitingembodiments, it is to be understood that such detail is solely for thatpurpose and that they are not limited to the disclosed embodiments but,on the contrary, is intended to cover modifications and equivalentarrangements that are within the spirit and scope of the appendedclaims. For example, it is to be understood that the present disclosurecontemplates that, to the extent possible, one or more features of anyembodiment can be combined with one or more features of any otherembodiment.

What is claimed is:
 1. A computer-implemented method for generatingreal-time predictions for vehicle transportation requests using machinelearning, comprising: generating, with at least one processor and amachine-learning classification model, a transportation categorizationfor each consumer of a plurality of consumers based at least partiallyon historic transaction data comprising a plurality of transactions byeach of the plurality of consumers, each plurality of transactions foreach consumer including a subset of transactions for vehicletransportation; processing, with at least one processor, a plurality ofnew transactions by each consumer of the plurality of consumers, eachnew transaction of the plurality of new transactions associated with atleast one geographic node of activity; in response to processing theplurality of new transactions, generating, with at least one processorand the machine-learning classification model, a plurality of vehicletransportation predictions for the plurality of consumers, each vehicletransportation prediction based on the transportation categorization foreach consumer and a geographic node of activity associated with a newtransaction, each vehicle transportation prediction representing alikelihood that the consumer will request vehicle transportationsubsequent to conducting the new transaction; and generating, with atleast one processor, a supply map interface comprising at least onevisual identification of at least one location in which a number ofrequests for vehicle transportation is predicted to increase based onthe plurality of vehicle transportation predictions.
 2. Thecomputer-implemented method of claim 1, wherein generating thetransportation categorization for each consumer is based at leastpartially on a transaction type.
 3. The computer-implemented method ofclaim 2, wherein the transaction type is determined based on at leastone of the following: a merchant category, a merchant identity, atransaction amount, a transaction frequency, or any combination thereof.4. The computer-implemented method of claim 1, wherein generating thetransportation categorization for each consumer is based at leastpartially on condition data for the plurality of transactions, thecondition data comprising at least one of the following: a time of day,a day of the week, a day of the year, a weather condition, a proximityto a specified location, a proximity to a specified event, or anycombination thereof.
 5. The computer-implemented method of claim 1,further comprising automatically dispatching, with at least oneprocessor, at least one vehicle to the at least one location.
 6. Thecomputer-implemented method of claim 1, wherein the geographic node ofactivity corresponds to a location of at least one point-of-saleterminal.
 7. The computer-implemented method of claim 1, furthercomprising: receiving, by at least one processor, location dataassociated with a geographic location of at least one vehicle-for-hire;and associating, by at least one processor, the geographic location ofthe at least one vehicle-for-hire with at least one geographic node ofactivity based on a proximity of the vehicle-for-hire to the at leastone geographic node of activity, wherein the at least one visualidentification of the at least one location in which a number ofrequests for vehicle transportation is predicted to increase is furthergenerated based on a number of vehicles-for-hire that is associated withthe at least one geographic node of activity corresponding to the atleast one location.
 8. A system for generating real-time predictions forvehicle transportation requests using machine learning, comprising atleast one processor programmed and/or configured to: generate, with amachine-learning classification model, a transportation categorizationfor each consumer of a plurality of consumers based at least partiallyon historic transaction data comprising a plurality of transactions byeach of the plurality of consumers, each plurality of transactions foreach consumer including a subset of transactions for vehicletransportation; process a plurality of new transactions by each consumerof the plurality of consumers, each new transaction of the plurality ofnew transactions associated with at least one geographic node ofactivity; in response to processing the plurality of new transactions,generate, with machine-learning classification model, a plurality ofvehicle transportation predictions for the plurality of consumers, eachvehicle transportation prediction based on the transportationcategorization for each consumer and a geographic node of activityassociated with a new transaction, each vehicle transportationprediction representing a likelihood that the consumer will requestvehicle transportation subsequent to conducting the new transaction; andgenerate a supply map interface comprising at least one visualidentification of at least one location in which a number of requestsfor vehicle transportation is predicted to increase based on theplurality of vehicle transportation predictions.
 9. The system of claim8, wherein the at least one processor is programmed and/or configured togenerate the transportation categorization for each consumer based atleast partially on a transaction type.
 10. The system of claim 9,wherein the at least one processor is programmed and/or configured todetermine the transaction type based on at least one of the following: amerchant category, a merchant identity, a transaction amount, atransaction frequency, or any combination thereof.
 11. The system ofclaim 8, wherein the at least one processor is programmed and/orconfigured to generate the transportation categorization for eachconsumer is based at least partially on condition data for the pluralityof transactions, the condition data comprising at least one of thefollowing: a time of day, a day of the week, a day of the year, aweather condition, a proximity to a specified location, a proximity to aspecified event, or any combination thereof.
 12. The system of claim 8,wherein the at least one processor is further programmed and/orconfigured to automatically dispatch at least one vehicle to the atleast one location.
 13. The system of claim 8, wherein the geographicnode of activity corresponds to a location of at least one point-of-saleterminal.
 14. The system of claim 8, wherein the at least one processoris further programmed and/or configured to: receive location dataassociated with a geographic location of at least one vehicle-for-hire;and associate the geographic location of the at least onevehicle-for-hire with at least one geographic node of activity based ona proximity of the vehicle-for-hire to the at least one geographic nodeof activity, wherein the at least one visual identification of the atleast one location in which a number of requests for vehicletransportation is predicted to increase is further generated based on anumber of vehicles-for-hire that is associated with the at least onegeographic node of activity corresponding to the at least one location.15. A computer program product for generating real-time predictions forvehicle transportation requests using machine learning, comprising atleast one non-transitory computer-readable medium including programinstructions, which, when executed by at least one processer, cause theat least one processor to: generate, with a machine-learningclassification model, a transportation categorization for each consumerof a plurality of consumers based at least partially on historictransaction data comprising a plurality of transactions by each of theplurality of consumers, each plurality of transactions for each consumerincluding a subset of transactions for vehicle transportation; process aplurality of new transactions by each consumer of the plurality ofconsumers, each new transaction of the plurality of new transactionsassociated with at least one geographic node of activity; in response toprocessing the plurality of new transactions, generate, withmachine-learning classification model, a plurality of vehicletransportation predictions for the plurality of consumers, each vehicletransportation prediction based on the transportation categorization foreach consumer and a geographic node of activity associated with a newtransaction, each vehicle transportation prediction representing alikelihood that the consumer will request vehicle transportationsubsequent to conducting the new transaction; and generate a supply mapinterface comprising at least one visual identification of at least onelocation in which a number of requests for vehicle transportation ispredicted to increase based on the plurality of vehicle transportationpredictions.
 16. The computer program product of claim 15, furthercomprising instructions, which, when executed by at least one processer,cause the at least one processor to: generate the transportationcategorization for each consumer based at least partially on atransaction type, wherein the transaction type is determined based on atleast one of the following: a merchant category, a merchant identity, atransaction amount, a transaction frequency, or any combination thereof.17. The computer program product of claim 15, further comprisinginstructions, which, when executed by at least one processer, cause theat least one processor to: generate the transportation categorizationfor each consumer is based at least partially on condition data for theplurality of transactions, the condition data comprising at least one ofthe following: a time of day, a day of the week, a day of the year, aweather condition, a proximity to a specified location, a proximity to aspecified event, or any combination thereof.
 18. The computer programproduct of claim 15, further comprising instructions, which, whenexecuted by at least one processer, cause the at least one processor toautomatically dispatch at least one vehicle to the at least onelocation.
 19. The computer program product of claim 15, wherein thegeographic node of activity corresponds to a location of at least onepoint-of-sale terminal.
 20. The computer program product of claim 15,further comprising instructions, which, when executed by at least oneprocesser, cause the at least one processor to: receive location dataassociated with a geographic location of at least one vehicle-for-hire;and associate the geographic location of the at least onevehicle-for-hire with at least one geographic node of activity based ona proximity of the vehicle-for-hire to the at least one geographic nodeof activity, wherein the at least one visual identification of the atleast one location in which a number of requests for vehicletransportation is predicted to increase is further generated based on anumber of vehicles-for-hire that is associated with the at least onegeographic node of activity corresponding to the at least one location.